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# Data Modeler - Analytical Solutions

- **Company:** ITRANSITION, INC.
- **Location:** United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Business Logic, Information Engineering, Data Integrity, Data Profiling, Data Vault Modeling, Python (Programming Language), SQL Databases, Data Strategy, Semi-structured Data, Data Lineage, Data Management, Databricks
- **Published:** June 22, 2026
- **Apply:** https://www.itransition.com/careers/senior-data-modeller

## About the Role

* 5+ years of dedicated experience in data modelling (not deep hands-on engineering)
 * Hands-on experience with modern cloud data platforms (Databricks)
 * Strong experience with SQL. Familiarity with Python/PySpark is a plus
 * Deep knowledge of modeling approaches such as Kimball, Inmon, and Data Vault 2.0, with the ability to select the most appropriate methodology to address specific business challenges
 * Experience with enterprise-grade modelling tools (e.g., ER/Studio, Erwin, or Sparx EA)
 * Strong soft skills and non-confrontational behavior

## Description

We are seeking a talented Senior Data Modeler - Analytical Solutions to lead the design of enterprise-scale analytical Data Vault 2.0 models for a data platform built on Databricks. We are looking for a candidate whose expertise is rooted in foundational data modelling principles rather than a single vendor. In this role, you will define shared analytical models and standards that ensure consistency, reusability, and trust across the enterprise. This position offers a unique opportunity to lead the architectural transformation of a complex legacy environment into a streamlined, future-ready data asset, while building a versatile profile to drive data strategy across any modern cloud environment.
office remoteEuropean UnionPoland, * Model enterprise-level analytical Data Vault 2.0 structures on Databricks, handling both structured and semi-structured data

 * Create and maintain data mapping artifacts to support analytics, integration, and migration initiatives
 * Establish modeling patterns suitable for lakehouse, medallion, and semantic-layer architectures
 * Ensure alignment between physical analytical models and enterprise semantic definitions
 * Act as the primary interface between business stakeholders and data engineering, focusing strictly on data modeling rather than hands-on engineering
 * Translate complex business logic into high-quality documentation, data lineage, and precise models for engineering implementation
 * Conduct deep-dive sessions to audit and remediate complex or poorly structured data assets in the current environment
 * Establish enterprise data modeling standards that prioritize data integrity, portability, and long-term flexibility
 * Lead data profiling activities to ensure physical implementations align with defined data models


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